ABSTRACT Regular monitoring and mapping of rice (Oryza Sativa) growth phases are essential for industry stakeholders to ensure food production is on track and to assess the impact of climate change on rice production. In Indonesia, high-cost field surveys have been widely used to monitor the rice growth phases. Alternatively, this research proposes a methodology to retrieve multi-temporal rice phenology (vegetative, reproductive, and ripening) and bare land mapping using medium resolution remote sensing imagery obtained from Landsat-8 Operational Land Imager (OLI) combined with machine learning techniques. In this study, we have used extensive ground validation information collected from 2014 to 2016 for training the models. This ground validation information was obtained from pre-installed webcams across Indonesia. Five different machine learning algorithms were used including random forest (RF), support vector machine (SVM) with three kernel functions (linear, polynomial, and radial) and artificial neural networks (ANN) to classify rice growth phases and bare land. This paper also evaluates the temporal evolution of rice phenology and bare land to check the prediction model consistency between two consecutive dates in 3 years. The results show that the nonlinear SVM algorithm gives the best model accuracy (70.5% with Kappa: 0.66) based on the test dataset and the lowest temporal changes (<11%). Spatial-temporal assessment of rice phenology and bare land from Landsat-8 indicated that the models were reliable and robust over different seasons and years. The distribution of rice phenology maps will enable Indonesian management authorities to supply fertilizer, allocate water resources, harvesting, and marketing facilities more efficiently.

中文翻译：

---

使用机器学习的 Landsat-8 OLI 绘制水稻生长阶段和裸地图

摘要 水稻（Oryza Sativa）生长阶段的定期监测和测绘对于行业利益相关者确保粮食生产正常进行和评估气候变化对水稻生产的影响至关重要。在印度尼西亚，高成本的实地调查已被广泛用于监测水稻生长阶段。或者，本研究提出了一种方法，利用从 Landsat-8 操作陆地成像仪 (OLI) 获得的中分辨率遥感图像结合机器学习技术来检索多时相水稻物候（营养、繁殖和成熟）和裸地测绘。在这项研究中，我们使用了从 2014 年到 2016 年收集的大量地面验证信息来训练模型。该地面验证信息是从印度尼西亚预装的网络摄像头中获得的。使用五种不同的机器学习算法，包括随机森林 (RF)、具有三个核函数（线性、多项式和径向）的支持向量机 (SVM) 和人工神经网络 (ANN) 对水稻生长阶段和裸地进行分类。本文还评估了水稻物候和裸地的时间演变，以检查 3 年内连续两个日期之间的预测模型一致性。结果表明，非线性 SVM 算法基于测试数据集提供了最佳模型精度（70.5%，Kappa：0.66）和最低的时间变化（<11%）。Landsat-8 对水稻物候和裸地的时空评估表明模型在不同季节和年份中是可靠和稳健的。